deployment finder.py

# Deployment finder
# A script to list all the Azure OpenAI model deployments in an Azure subscription

# Import libraries, and load the Azure Subscription ID
import os
from azure.identity import DefaultAzureCredential
from azure.mgmt.cognitiveservices import CognitiveServicesManagementClient
from azure.mgmt.resource import ResourceManagementClient
import webbrowser
import pandas as pd
from dotenv import load_dotenv

load_dotenv()

# Get the Azure subscription ID from the .env file
subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]

# Authenticate with Azure
credential = DefaultAzureCredential()

# Create a Cognitive Services Management client
client = CognitiveServicesManagementClient(credential, subscription_id)

# Initialize the Resource Management client
resource_client = ResourceManagementClient(credential, subscription_id)

# Initialize an empty DataFrame with the specified columns
deployment_df = pd.DataFrame(columns=["Resource Group", "OpenAI Service Name", "Deployment Name", "Model Name", "Version"])

# Filter for OpenAI instances and list model deployments
for resource_group in resource_client.resource_groups.list():
    accounts = client.accounts.list_by_resource_group(resource_group.name)  # Corrected line
    for account in accounts:
        if account.kind == 'OpenAI':
            # Correctly access deployment properties using dot notation
            deployments = client.deployments.list(resource_group.name, account.name)
            deployment_info = [{"deploymentname": deployment.name, "modelname": deployment.properties.model.name, "version": deployment.properties.model.version} for deployment in deployments]
            
            # output deployment_info in a more readable format
            for deployment in deployment_info:
                #print(f"Deployment Name: {deployment['deploymentname']}, Model Name: {deployment['modelname']}, Version: {deployment['version']}")
                # Inside the loop where you add rows to the DataFrame
                new_row = pd.DataFrame([{
                    "Resource Group": resource_group.name,
                    "OpenAI Service Name": account.name,
                    "Deployment Name": deployment['deploymentname'],
                    "Model Name": deployment['modelname'],
                    "Version": deployment['version']
                }])
                
                deployment_df = pd.concat([deployment_df, new_row], ignore_index=True)

# Optionally, display the DataFrame
#print(deployment_df)

# Convert the DataFrame to HTML
html_string = deployment_df.to_html()

# Define the path for the HTML file (e.g., in the current working directory)
html_file_path = os.path.join(os.getcwd(), "deployments.html")

# Write the HTML string to the file
with open(html_file_path, 'w') as f:
    f.write(html_string)

# Open the HTML file in the default browser
webbrowser.open('file://' + html_file_path)